CN103063167A - Method for judging laser cleaning effect automatically - Google Patents

Abstract

The invention discloses a method for automatically judging the effect of laser cleaning. The method judges the cleaning effect by comparing the RGB average value of the color image of the metal surface after rust removal with that of the color image of the metal surface without rust. The grayscale image of the metal surface is compared with the average value of the grayscale image of the uncorroded metal surface to judge the cleaning efficiency. This method realizes the automatic judgment of the laser cleaning effect.

Description

A method for automatically judging laser cleaning effect

technical field

The invention belongs to the field of laser cleaning, in particular to a method for automatically judging the effect of laser cleaning.

technical background

During the laser cleaning process, due to the different corrosion degrees of the samples, there may be corrosion pits, so a single cleaning has different effects. Some parts of the sample surface are cleaned, and some parts still have corrosion layers. The cleaning effect can be judged by the cleaning efficiency, which is defined as the cleaned area divided by the total cleaned area. At present, the effect of laser cleaning is mainly estimated by visual method. However, one of the advantages of laser cleaning is its high degree of automation, which is easy to connect with subsequent processes. Therefore, it is necessary to study a method for automatically judging the cleaning effect.

Contents of the invention

The object of the present invention is to provide a method for judging the effect of laser cleaning by using the image invention, which converts the calculation of the laser cleaning area into the statistics of the number of image pixels, and uses the image formed by the corrosion layer and the surface of the material under white light irradiation The average value and brightness range are different to realize automatic judgment.

The technical solution of the present invention is: a method for automatically judging the effect of laser cleaning, comprising the following steps:

；将所述基本彩色图像转换为基本灰度图像，计算所述基本灰度图像的灰度阈值

； 1) Irradiate the surface of the uncorroded metal material with a white light source, obtain the basic color image of the surface of the uncorroded metal material, and calculate the RGB mean value of the basic color image

;Convert the basic color image into a basic grayscale image, and calculate the grayscale threshold of the basic grayscale image

;

； 2) Irradiate the cleaned sample surface with a white light source, obtain a color image of the sample surface, preprocess the acquired color image, and calculate the RGB mean value of the color image

;

时判断为总体清洗干净。 3) Compare the RGB mean value of the color image described in step 2 with the RGB mean value of the basic color image described in step 1), when

It is judged to be clean as a whole.

与灰度阈值

比较，当时判断为该像素清洗干净。 4) Convert the color image described in step 2) into a grayscale image, and convert the grayscale of each pixel of the grayscale image

and grayscale threshold

compare when It is judged that the pixel is cleaned up.

。 5) Calculate the cleaning efficiency as the ratio of the number of pixels n cleaned in step 4) to the total number of pixels N of the grayscale image

.

为所述基本灰度图像的平均灰度值。 The grayscale threshold in step 1)

is the average gray value of the basic gray image.

Beneficial effects of the present invention: the color and brightness information of reflected light images of different substances under white light irradiation have different characteristics, the calculation of the area is converted into pixel calculation, and the purpose of automatically judging the cleaning effect is completed, which is conducive to the automation of laser cleaning equipment , It is also easy to integrate with other equipment, saving manpower and time, and the calculation method is simple, the calculation speed is fast, and the accuracy rate is high.

Description of drawings

Fig. 1 method flowchart;

Figure 2 schematic diagram of the device;

Figure 3 50W laser cleaning surface diagram;

1 laser; 2 optical system; 3 controller; 4 cleaning laser; 5 cleaning area; 6 cleaning material; 7 computer; 8 color array CCD; 9 white light source.

Detailed ways

The method of automatically judging the effect of laser cleaning mainly uses different materials. Under the irradiation of white light source, the color of reflected light and the illuminance information are different for detection. The average value of the three primary colors is used to judge whether the cleaning fails, and the area calculation is converted into a grayscale image. Pixel statistics to judge cleaning efficiency.

Figure 2 is a device for automatically judging the effect of laser cleaning, including a laser 1, an optical system 2, a controller 3, a computer 7, a color array CCD8 and a white light source 9, and the laser 1 and the optical system 2 are controlled by a controller 3 control, the optical system 2 collimates and converges the output laser light 4 of the laser 1 to the cleaning area 5 on the surface of the material 6 to be cleaned. The white light source 9 irradiates the same cleaning area 5, the reflected light is received by the color array CCD 8, and the computer 7 calculates the cleaning effect.

和灰度图像阈值

。由于清洗干净的金属材料表面呈现的是金属本色，且亮度均匀，因此采用白光光源照射金属材料干净表面，彩色面阵CCD拍摄表面图像，进行RGB（红绿蓝）三基色分析，计算图像RGB均值阈值

，此阈值接近于金属颜色的RGB值。再将彩色图像进行转换为灰度图像，将所有像素的灰度值加和，再除以像素数，即得

，此阈值也接近为一恒值。 Figure 1 is a flow chart of a method for automatically judging the effect of laser cleaning. Before automatic judgment, it is necessary to obtain the average value of the three primary colors of the surface image of the uncorroded metal material

and grayscale image threshold

. Since the surface of the cleaned metal material presents the original color of the metal, and the brightness is uniform, the clean surface of the metal material is irradiated with a white light source, and the surface image is taken by the color area array CCD, and the RGB (red, green and blue) three primary colors are analyzed to calculate the RGB average value of the image threshold

, this threshold is close to the RGB value of the metallic color. Then convert the color image to a grayscale image, add the grayscale values of all pixels, and then divide by the number of pixels to get

, this threshold is also close to a constant value.

有可能接近于；第二种情况，当材料被清洗干净后，呈现的是金属自身的光泽，且由于反射率大大提高，亮度也比清洗前提高很多，因此这时候的RGB均值

接近或稍大于金属颜色的RGB值；第三种情况，当清洗过度时，表面材料发生质变，材料呈现的也不再是金属本身颜色，应为其生成物的颜色，这时候的RGB均值

接近于生成物颜色的RGB值，也就是说，可通过颜色信息判断是否清洗干净。所以将RGB均值与均值阈值比较，当

时，说明清洗干净，否则表示清洗失败。将清洗干净的彩色图像转换为灰度图像，像素总素为N，用n表示清洗干净的像素个数。将灰度图像的每一像素

逐个与灰度阈值

比较，当，说明清洗干净， n值加1，最终清洗效率

。当清洗效率达到某一数值时，符合清洗要求。 After cleaning, the white light source is used to irradiate the cleaned sample area, the color area array CCD captures the cleaned surface image, and the contrast enhancement method is used to preprocess the collected image. Also perform RGB (red, green and blue) three-primary color analysis, and calculate the RGB mean value of the image . In the first case, when the metal material covers the corrosion layer, the RGB mean value at this time The RGB value is close to the color of the corrosion layer. Due to the rough surface, most of the light is scattered, showing a shadow area. At this time, the RGB average value

likely to be close to ; In the second case, when the material is cleaned, it presents the luster of the metal itself, and because the reflectivity is greatly improved, the brightness is also much higher than before cleaning, so the RGB average at this time

It is close to or slightly greater than the RGB value of the metal color; in the third case, when the cleaning is excessive, the surface material undergoes a qualitative change, and the material is no longer the color of the metal itself, but it should be the color of the product. The RGB average value at this time

It is close to the RGB value of the color of the product, that is to say, it can be judged whether it is cleaned or not through the color information. So comparing the RGB mean with the mean threshold, when

, it means the cleaning is clean, otherwise it means the cleaning failed. Convert the cleaned color image to a grayscale image, the total number of pixels is N, and n represents the number of cleaned pixels. Convert each pixel of the grayscale image to

One by one with grayscale thresholding

compare when , indicating that the cleaning is clean, adding 1 to the n value, the final cleaning efficiency

. When the cleaning efficiency reaches a certain value, it meets the cleaning requirements.

Example

Firstly, the clean marine steel surface is irradiated with a white light source to obtain the surface image, and the contrast of the image is adjusted to 100%, and the mean threshold value [248, 245, 249] and the gray threshold value 232 of the image RGB three primary colors are obtained.

Using a 1064nm 100W fiber pulse laser with a pulse repetition frequency of 100kHz and an overlap rate of 50%, the collimating and focusing optical system directly uses a scanning galvanometer and a 254nm focusing lens to clean the surface of marine steel with 10W, 50W, and 100W laser energy. , the visual result is that the 10W laser effect is not cleaned, the 50W laser is basically cleaned, and the 100W laser is over-cleaned. Figure 3 is the surface diagram of the 50W laser cleaning material. After collecting the images separately, first adjust the contrast of the image to 100%, store the image information, and obtain the average value of the RGB three primary colors of the image, and calculate the average value of the image with rust to [123,45,20], indicating that the color of the rust is red ;The 10W laser image is [38,35,30], indicating that the image is close to black and not cleaned; the 50W laser image is [207,214,204], indicating that the image is close to white and basically cleaned; the 100W laser image is [ 103,113,91], indicating that the image is close to yellow and has been over-cleaned. Compared with the average threshold [248,245,249], it is greater than the range comparison. Only the 50W laser action image meets the requirements, and it is directly converted into a grayscale image. The brightness threshold is 232. Compared one by one, the number of cleaned is 45527, and the total pixels are 61740. Therefore, the cleaning efficiency is 72.74%.

Claims (3)

1. A method for automatically judging laser cleaning effect, is characterized in that, comprises the following steps: 1) Irradiate the surface of the uncorroded metal material with a white light source, obtain the basic color image of the surface of the uncorroded metal material, and calculate the RGB mean value of the basic color image

;Convert the basic color image into a basic grayscale image, and calculate the grayscale threshold of the basic grayscale image

; 2) Irradiate the cleaned sample surface with a white light source, obtain a color image of the sample surface, preprocess the acquired color image, and calculate the RGB mean value of the color image

; 3) Compare the RGB mean value of the color image described in step 2 with the RGB mean value of the basic color image described in step 1), when

It is judged to be clean as a whole. 2. A method for automatically judging the effect of laser cleaning according to claim 1, characterized in that, after the step 3), it also includes: 4) Convert the color image described in step 2) into a grayscale image, and convert the grayscale of each pixel of the grayscale image

and grayscale threshold

compare when When it is judged that the pixel is cleaned; 5) Calculate the cleaning efficiency as the ratio of the number of pixels n cleaned in step 4) to the total number of pixels N of the grayscale image

. 3. A method for automatically judging the effect of laser cleaning according to claim 1, characterized in that the gray threshold in step 1) is the average gray value of the basic gray image.

Images